




import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
UpperCAmelCase_		:					List[str]          =  pd.read_csv(
    "https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
    "position_salaries.csv"
)
UpperCAmelCase_		:					Union[str, Any]          =  dataset.iloc[:, 1:2].values
UpperCAmelCase_		:					Dict          =  dataset.iloc[:, 2].values


UpperCAmelCase_    ,  UpperCAmelCase_    ,  UpperCAmelCase_    ,  UpperCAmelCase_		:					Dict          =  train_test_split(X, y, test_size=0.2, random_state=0)


UpperCAmelCase_		:					Union[str, Any]          =  PolynomialFeatures(degree=4)
UpperCAmelCase_		:					Any          =  poly_reg.fit_transform(X)
UpperCAmelCase_		:					Optional[Any]          =  LinearRegression()
pol_reg.fit(X_poly, y)




def 		lowerCAmelCase_ (						):
				plt.scatter(lowerCamelCase						,  lowerCamelCase						,  color="""red""" )
				plt.plot(lowerCamelCase						,  pol_reg.predict(poly_reg.fit_transform(lowerCamelCase ) )						,  color="""blue""" )
				plt.title("""Truth or Bluff (Linear Regression)""" )
				plt.xlabel("""Position level""" )
				plt.ylabel("""Salary""" )
				plt.show()


if __name__ == "__main__":
							viz_polymonial()

							# Predicting a new result with Polymonial Regression
							pol_reg.predict(poly_reg.fit_transform([[5.5]]))
							# output should be 132148.43750003












"""simple docstring"""


from abc import ABC, abstractmethod
from argparse import ArgumentParser




class 						__UpperCamelCase     (     UpperCamelCase						):
					@staticmethod
					@abstractmethod
					def        UpperCAmelCase__ (							UpperCAmelCase							:	ArgumentParser   )	->       Optional[Any]:
											raise NotImplementedError()
					@abstractmethod
					def        UpperCAmelCase__ (							self							:	str   )	->       List[str]:
											raise NotImplementedError()